2020-08-22 19:42:14 +08:00
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# paddleocr package使用说明
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## 快速上手
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### 安装whl包
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pip安装
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```bash
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pip install paddleocr
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```
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本地构建并安装
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```bash
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python setup.py bdist_wheel
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2020-08-23 16:38:01 +08:00
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pip install dist/paddleocr-0.0.3-py3-none-any.whl
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2020-08-22 19:42:14 +08:00
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```
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### 1. 代码使用
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* 检测+识别全流程
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```python
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from paddleocr import PaddleOCR, draw_ocr
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ocr = PaddleOCR(model_storage_directory='./model') # need to run only once to load model into memory
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img_path = 'PaddleOCR/doc/imgs/11.jpg'
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result = ocr.ocr(img_path)
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for line in result:
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print(line)
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# 显示结果
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from PIL import Image
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image = Image.open(img_path).convert('RGB')
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boxes = [line[0] for line in result]
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txts = [line[1][0] for line in result]
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scores = [line[1][1] for line in result]
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im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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结果是一个list，每个item包含了文本框，文字和识别置信度
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```bash
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[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
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[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
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[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
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[[[22.0, 140.0], [284.0, 140.0], [284.0, 167.0], [22.0, 167.0]], ['每瓶22元，1000瓶起订）', 0.97444016]]
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[[[22.0, 174.0], [85.0, 174.0], [85.0, 198.0], [22.0, 198.0]], ['【品牌】', 0.8187138]]
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[[[89.0, 176.0], [301.0, 176.0], [301.0, 196.0], [89.0, 196.0]], ['：代加工方式/OEMODM', 0.9421848]]
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[[[23.0, 205.0], [85.0, 205.0], [85.0, 229.0], [23.0, 229.0]], ['【品名】', 0.76008326]]
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[[[88.0, 204.0], [235.0, 206.0], [235.0, 229.0], [88.0, 227.0]], ['：纯臻营养护发素', 0.9633639]]
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[[[23.0, 236.0], [121.0, 236.0], [121.0, 261.0], [23.0, 261.0]], ['【产品编号】', 0.84101385]]
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[[[110.0, 239.0], [239.0, 239.0], [239.0, 256.0], [110.0, 256.0]], ['1：YM-X-3011', 0.8621878]]
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[[[414.0, 233.0], [430.0, 233.0], [430.0, 304.0], [414.0, 304.0]], ['ODM OEM', 0.9084018]]
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[[[23.0, 268.0], [183.0, 268.0], [183.0, 292.0], [23.0, 292.0]], ['【净含量】：220ml', 0.9278281]]
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[[[24.0, 301.0], [118.0, 301.0], [118.0, 321.0], [24.0, 321.0]], ['【适用人群】', 0.90901047]]
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[[[127.0, 300.0], [254.0, 300.0], [254.0, 323.0], [127.0, 323.0]], ['：适合所有肤质', 0.95465785]]
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[[[24.0, 332.0], [117.0, 332.0], [117.0, 353.0], [24.0, 353.0]], ['【主要成分】', 0.88936955]]
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[[[139.0, 332.0], [236.0, 332.0], [236.0, 352.0], [139.0, 352.0]], ['鲸蜡硬脂醇', 0.9447544]]
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[[[248.0, 332.0], [345.0, 332.0], [345.0, 352.0], [248.0, 352.0]], ['燕麦B-葡聚', 0.89748293]]
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[[[54.0, 363.0], [232.0, 363.0], [232.0, 383.0], [54.0, 383.0]], [' 椰油酰胺丙基甜菜碱', 0.902023]]
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[[[25.0, 364.0], [64.0, 364.0], [64.0, 383.0], [25.0, 383.0]], ['糖、', 0.985203]]
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[[[244.0, 363.0], [281.0, 363.0], [281.0, 382.0], [244.0, 382.0]], ['泛服', 0.44537082]]
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[[[367.0, 367.0], [475.0, 367.0], [475.0, 388.0], [367.0, 388.0]], ['（成品包材）', 0.9834532]]
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[[[24.0, 395.0], [120.0, 395.0], [120.0, 416.0], [24.0, 416.0]], ['【主要功能】', 0.88684446]]
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[[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]], ['：可紧致头发磷层', 0.9342501]]
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[[[265.0, 395.0], [361.0, 395.0], [361.0, 415.0], [265.0, 415.0]], ['琴，从而达到', 0.8253762]]
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[[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]], ['即时持久改善头发光泽的效果，给干燥的头', 0.97785276]]
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[[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]], ['发足够的滋养', 0.9577897]]
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```
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结果可视化
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<div align="center">
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<img src="../imgs_results/whl/11_det_rec.jpg" width="800">
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</div>
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* 单独执行检测
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```python
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from paddleocr import PaddleOCR, draw_ocr
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ocr = PaddleOCR(model_storage_directory='./model') # need to run only once to load model into memory
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img_path = 'PaddleOCR/doc/imgs/11.jpg'
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result = ocr.ocr(img_path,rec=False)
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for line in result:
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print(line)
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# 显示结果
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from PIL import Image
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image = Image.open(img_path).convert('RGB')
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im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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结果是一个list，每个item只包含文本框
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```bash
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[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
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[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
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[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
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[[265.0, 395.0], [361.0, 395.0], [361.0, 415.0], [265.0, 415.0]]
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[[24.0, 395.0], [120.0, 395.0], [120.0, 416.0], [24.0, 416.0]]
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[[367.0, 367.0], [475.0, 367.0], [475.0, 388.0], [367.0, 388.0]]
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[[54.0, 363.0], [232.0, 363.0], [232.0, 383.0], [54.0, 383.0]]
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[[25.0, 364.0], [64.0, 364.0], [64.0, 383.0], [25.0, 383.0]]
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[[244.0, 363.0], [281.0, 363.0], [281.0, 382.0], [244.0, 382.0]]
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[[248.0, 332.0], [345.0, 332.0], [345.0, 352.0], [248.0, 352.0]]
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[[139.0, 332.0], [236.0, 332.0], [236.0, 352.0], [139.0, 352.0]]
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[[24.0, 332.0], [117.0, 332.0], [117.0, 353.0], [24.0, 353.0]]
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[[127.0, 300.0], [254.0, 300.0], [254.0, 323.0], [127.0, 323.0]]
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[[24.0, 301.0], [118.0, 301.0], [118.0, 321.0], [24.0, 321.0]]
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[[23.0, 268.0], [183.0, 268.0], [183.0, 292.0], [23.0, 292.0]]
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[[110.0, 239.0], [239.0, 239.0], [239.0, 256.0], [110.0, 256.0]]
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[[23.0, 236.0], [121.0, 236.0], [121.0, 261.0], [23.0, 261.0]]
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[[414.0, 233.0], [430.0, 233.0], [430.0, 304.0], [414.0, 304.0]]
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[[88.0, 204.0], [235.0, 206.0], [235.0, 229.0], [88.0, 227.0]]
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[[23.0, 205.0], [85.0, 205.0], [85.0, 229.0], [23.0, 229.0]]
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[[89.0, 176.0], [301.0, 176.0], [301.0, 196.0], [89.0, 196.0]]
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[[22.0, 174.0], [85.0, 174.0], [85.0, 198.0], [22.0, 198.0]]
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[[22.0, 140.0], [284.0, 140.0], [284.0, 167.0], [22.0, 167.0]]
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[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]]
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[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]]
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[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]]
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```
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结果可视化
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<div align="center">
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<img src="../imgs_results/whl/11_det.jpg" width="800">
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</div>
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* 单独执行识别
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```python
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from paddleocr import PaddleOCR
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ocr = PaddleOCR(model_storage_directory='./model') # need to run only once to load model into memory
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img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
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result = ocr.ocr(img_path,det=False)
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for line in result:
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print(line)
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```
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结果是一个list，每个item只包含识别结果和识别置信度
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```bash
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['韩国小馆', 0.9907421]
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```
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### 通过命令行使用
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查看帮助信息
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```bash
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paddleocr -h
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```
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* 检测+识别全流程
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```bash
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paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
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```
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结果是一个list，每个item包含了文本框，文字和识别置信度
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```bash
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[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
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[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
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[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
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[[[22.0, 140.0], [284.0, 140.0], [284.0, 167.0], [22.0, 167.0]], ['每瓶22元，1000瓶起订）', 0.97444016]]
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[[[22.0, 174.0], [85.0, 174.0], [85.0, 198.0], [22.0, 198.0]], ['【品牌】', 0.8187138]]
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[[[89.0, 176.0], [301.0, 176.0], [301.0, 196.0], [89.0, 196.0]], ['：代加工方式/OEMODM', 0.9421848]]
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[[[23.0, 205.0], [85.0, 205.0], [85.0, 229.0], [23.0, 229.0]], ['【品名】', 0.76008326]]
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[[[88.0, 204.0], [235.0, 206.0], [235.0, 229.0], [88.0, 227.0]], ['：纯臻营养护发素', 0.9633639]]
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[[[23.0, 236.0], [121.0, 236.0], [121.0, 261.0], [23.0, 261.0]], ['【产品编号】', 0.84101385]]
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[[[110.0, 239.0], [239.0, 239.0], [239.0, 256.0], [110.0, 256.0]], ['1：YM-X-3011', 0.8621878]]
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[[[414.0, 233.0], [430.0, 233.0], [430.0, 304.0], [414.0, 304.0]], ['ODM OEM', 0.9084018]]
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[[[23.0, 268.0], [183.0, 268.0], [183.0, 292.0], [23.0, 292.0]], ['【净含量】：220ml', 0.9278281]]
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[[[24.0, 301.0], [118.0, 301.0], [118.0, 321.0], [24.0, 321.0]], ['【适用人群】', 0.90901047]]
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[[[127.0, 300.0], [254.0, 300.0], [254.0, 323.0], [127.0, 323.0]], ['：适合所有肤质', 0.95465785]]
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[[[24.0, 332.0], [117.0, 332.0], [117.0, 353.0], [24.0, 353.0]], ['【主要成分】', 0.88936955]]
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[[[139.0, 332.0], [236.0, 332.0], [236.0, 352.0], [139.0, 352.0]], ['鲸蜡硬脂醇', 0.9447544]]
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[[[248.0, 332.0], [345.0, 332.0], [345.0, 352.0], [248.0, 352.0]], ['燕麦B-葡聚', 0.89748293]]
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[[[54.0, 363.0], [232.0, 363.0], [232.0, 383.0], [54.0, 383.0]], [' 椰油酰胺丙基甜菜碱', 0.902023]]
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[[[25.0, 364.0], [64.0, 364.0], [64.0, 383.0], [25.0, 383.0]], ['糖、', 0.985203]]
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[[[244.0, 363.0], [281.0, 363.0], [281.0, 382.0], [244.0, 382.0]], ['泛服', 0.44537082]]
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[[[367.0, 367.0], [475.0, 367.0], [475.0, 388.0], [367.0, 388.0]], ['（成品包材）', 0.9834532]]
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[[[24.0, 395.0], [120.0, 395.0], [120.0, 416.0], [24.0, 416.0]], ['【主要功能】', 0.88684446]]
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[[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]], ['：可紧致头发磷层', 0.9342501]]
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[[[265.0, 395.0], [361.0, 395.0], [361.0, 415.0], [265.0, 415.0]], ['琴，从而达到', 0.8253762]]
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[[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]], ['即时持久改善头发光泽的效果，给干燥的头', 0.97785276]]
|
|
|
|
|
|
[[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]], ['发足够的滋养', 0.9577897]]
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
* 单独执行检测
|
|
|
|
|
|
```bash
|
|
|
|
|
|
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
|
|
|
|
|
|
```
|
|
|
|
|
|
结果是一个list，每个item只包含文本框
|
|
|
|
|
|
```bash
|
|
|
|
|
|
[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
|
|
|
|
|
|
[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
|
|
|
|
|
|
[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
|
|
|
|
|
|
[[265.0, 395.0], [361.0, 395.0], [361.0, 415.0], [265.0, 415.0]]
|
|
|
|
|
|
[[24.0, 395.0], [120.0, 395.0], [120.0, 416.0], [24.0, 416.0]]
|
|
|
|
|
|
[[367.0, 367.0], [475.0, 367.0], [475.0, 388.0], [367.0, 388.0]]
|
|
|
|
|
|
[[54.0, 363.0], [232.0, 363.0], [232.0, 383.0], [54.0, 383.0]]
|
|
|
|
|
|
[[25.0, 364.0], [64.0, 364.0], [64.0, 383.0], [25.0, 383.0]]
|
|
|
|
|
|
[[244.0, 363.0], [281.0, 363.0], [281.0, 382.0], [244.0, 382.0]]
|
|
|
|
|
|
[[248.0, 332.0], [345.0, 332.0], [345.0, 352.0], [248.0, 352.0]]
|
|
|
|
|
|
[[139.0, 332.0], [236.0, 332.0], [236.0, 352.0], [139.0, 352.0]]
|
|
|
|
|
|
[[24.0, 332.0], [117.0, 332.0], [117.0, 353.0], [24.0, 353.0]]
|
|
|
|
|
|
[[127.0, 300.0], [254.0, 300.0], [254.0, 323.0], [127.0, 323.0]]
|
|
|
|
|
|
[[24.0, 301.0], [118.0, 301.0], [118.0, 321.0], [24.0, 321.0]]
|
|
|
|
|
|
[[23.0, 268.0], [183.0, 268.0], [183.0, 292.0], [23.0, 292.0]]
|
|
|
|
|
|
[[110.0, 239.0], [239.0, 239.0], [239.0, 256.0], [110.0, 256.0]]
|
|
|
|
|
|
[[23.0, 236.0], [121.0, 236.0], [121.0, 261.0], [23.0, 261.0]]
|
|
|
|
|
|
[[414.0, 233.0], [430.0, 233.0], [430.0, 304.0], [414.0, 304.0]]
|
|
|
|
|
|
[[88.0, 204.0], [235.0, 206.0], [235.0, 229.0], [88.0, 227.0]]
|
|
|
|
|
|
[[23.0, 205.0], [85.0, 205.0], [85.0, 229.0], [23.0, 229.0]]
|
|
|
|
|
|
[[89.0, 176.0], [301.0, 176.0], [301.0, 196.0], [89.0, 196.0]]
|
|
|
|
|
|
[[22.0, 174.0], [85.0, 174.0], [85.0, 198.0], [22.0, 198.0]]
|
|
|
|
|
|
[[22.0, 140.0], [284.0, 140.0], [284.0, 167.0], [22.0, 167.0]]
|
|
|
|
|
|
[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]]
|
|
|
|
|
|
[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]]
|
|
|
|
|
|
[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]]
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
* 单独执行识别
|
|
|
|
|
|
```bash
|
|
|
|
|
|
paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --det false
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
结果是一个list，每个item只包含识别结果和识别置信度
|
|
|
|
|
|
```bash
|
|
|
|
|
|
['韩国小馆', 0.9907421]
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
## 参数说明
|
|
|
|
|
|
|
|
|
|
|
|
| 字段 | 说明 | 默认值 |
|
|
|
|
|
|
|-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|
|
|
|
|
|
|
| use_gpu | 是否使用GPU | TRUE |
|
|
|
|
|
|
| gpu_mem | 初始化占用的GPU内存大小 | 8000M |
|
|
|
|
|
|
| image_dir | 通过命令行调用时执行预测的图片或文件夹路径 | |
|
|
|
|
|
|
| det_algorithm | 使用的检测算法类型 | DB |
|
|
|
|
|
|
| det_model_name | 有两种使用方式： 1. 检测算法名称，此名称必须在支持列表内(目前只内置了ch_det_mv3_db)，传入错误参数时会显示支持的列表 2. 自己转换好的inference模型路径，此时模型路径下必须包含model和params文件。选择此方式时，需要手动指定det_algorithm的值 | ch_det_mv3_db |
|
|
|
|
|
|
| det_max_side_len | 检测算法前向时图片长边的最大尺寸，当长边超出这个值时会将长边resize到这个大小，短边等比例缩放 | 960 |
|
|
|
|
|
|
| det_db_thresh | DB模型输出预测图的二值化阈值 | 0.3 |
|
|
|
|
|
|
| det_db_box_thresh | DB模型输出框的阈值，低于此值的预测框会被丢弃 | 0.5 |
|
|
|
|
|
|
| det_db_unclip_ratio | DB模型输出框扩大的比例 | 2 |
|
|
|
|
|
|
| det_east_score_thresh | EAST模型输出预测图的二值化阈值 | 0.8 |
|
|
|
|
|
|
| det_east_cover_thresh | EAST模型输出框的阈值，低于此值的预测框会被丢弃 | 0.1 |
|
|
|
|
|
|
| det_east_nms_thresh | EAST模型输出框NMS的阈值 | 0.2 |
|
|
|
|
|
|
| rec_algorithm | 使用的识别算法类型 | CRNN |
|
|
|
|
|
|
| rec_model_name | 有两种使用方式： 1. 识别算法名称，此名称必须在支持列表内(目前支持CRNN,Rosetta,STAR,RARE等算法,但是内置的只有ch_rec_mv3_crnn_enhance)，传入错误参数时会显示支持的列表 2. 自己转换好的inference模型路径，此时模型路径下必须包含model和params文件。选择此方式时，需要手动指定rec_algorithm的值 | ch_rec_mv3_crnn_enhance |
|
|
|
|
|
|
| rec_image_shape | 识别算法的输入图片尺寸 | "3,32,320" |
|
|
|
|
|
|
| rec_char_type | 识别算法的字符类型，中文(ch)或英文(en) | ch |
|
|
|
|
|
|
| rec_batch_num | 进行识别时，同时前向的图片数 | 30 |
|
|
|
|
|
|
| rec_char_dict_path | 识别模型字典路径，当rec_model_name使用方式2传参时需要修改为自己的路径 | |
|
|
|
|
|
|
| use_space_char | 是否识别空格 | TRUE |
|
|
|
|
|
|
| enable_mkldnn | 是否启用mkldnn | FALSE |
|
|
|
|
|
|
| model_storage_directory | 下载模型保存路径 | ～/.paddleocr |
|
|
|
|
|
|
| det | 前向时使用启动检测 | TRUE |
|
|
|
|
|
|
| rec | 前向时是否启动识别 | TRUE |
|
